Predictive Factors for Procedure Time for Closure of Mucosal Defect Following Colorectal Endoscopic Submucosal Dissection

TL;DR

This study identifies factors affecting the time needed to close mucosal defects after colorectal endoscopic submucosal dissection.

Contribution

The study provides validated predictive factors for closure procedure time after colorectal ESD.

Findings

Resected specimen size significantly influences closure procedure time.

Colon site also affects the time required for mucosal defect closure.

Abstract

The present study was conducted to clarify predictive factors related to procedure time for closure of a mucosal defect following colorectal endoscopic submucosal dissection.

To prevent complications following a colorectal endoscopic submucosal dissection (ESD) procedure, closure of the resultant mucosal defect is considered to be most effective. However, closure after colorectal ESD is challenging, and technical difficulties can lead to a longer procedure time. Although it is important to clarify predictive factors related to the time needed for effective treatment planning, no such validated data obtained prior to the present study have been reported.

Overall, 61 consecutive patients who underwent colorectal ESD for a colorectal neoplasm sized greater than 20 mm were enrolled. Immediately after performing colorectal ESD, closure of the mucosal defect was implemented using a loop clip closure method. Factors with influence on closure procedure time were evaluated using multiple linear regression analyses.

Results obtained with a multiple linear regression model demonstrated that resected specimen size (β = 0.690, p < 0.01) and colon site (β = −0.209, p = 0.027) were factors with influence on the closure procedure. Those results were considered relevant to explain the 50.5% variance in time until completion of closure; thus, goodness of fit was considered to be high.

Findings obtained in this study were helpful to clarify predictive factors with influence on procedure time. The fit of the model was good, thus allowing for closure performance based on outcome prediction.

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